Search Results (783)

Cyanobacterial harmful algal blooms are an increasing concern for wildlife health, particularly in eutrophic wetlands, yet well-documented avian cases supported by environmental, pathological, and toxicological evidence remain scarce. This study describes a sentinel case of probable microcystin toxicosis in a Red-gartered coot (Fulica armillata) from Laguna Petrel, a protected coastal wetland in central Chile, during a broader wildlife mortality event. Surface-water monitoring included nutrient analyses, in situ physicochemical measurements, phytoplankton assessment, and cyanotoxin quantification. The evaluated bird was documented alive with severe motor impairment, euthanized, and examined by gross necropsy, histopathology, and tissue toxicology. Water analyses showed elevated nutrients, persistently alkaline and highly productive conditions, marked dominance of Microcystis aeruginosa, and high concentrations of microcystin-LR, microcystin-RR, microcystin-YR, and nodularin. The bird showed marked hepatic lesions at necropsy, histopathological changes compatible with acute hepatotoxic injury, and detectable microcystin-LR in lyophilized liver tissue. Taken together, these findings support a diagnosis of probable microcystin toxicosis in this individual. This case highlights the value of waterfowl as sentinels of ecosystem health threats and underscores the importance of integrated monitoring in protected coastal wetlands potentially affected by toxic cyanobacterial blooms. Full article

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

Algae rarely occur as solitary phototrophs in nature or engineering; instead, they are embedded in complex bacterial consortia that control their physiology, productivity and ecological performance. The phycosphere, a microscale niche rich in algal exudates, promotes extensive metabolic exchange and chemical signaling, defining these associations. Bacteria capitalize on the dissolved organic carbon released by algae, providing growth supporting molecules such as vitamins, trace metals, and siderophores, as well as regenerated inorganic nutrients. Bidirectional beneficial interactions range from obligate mutualism to facultative commensalism and antagonism, depending on environmental context and community membership. Bacterial partners can stimulate algal growth, morphogenesis, and stress tolerance, as well as modulating defense and programmed cell death during the decline and bloom succession of algae resulting from algicidal taxa. Metabolic cooperation, QS signaling, extracellular enzyme activity, and chemically induced gene expression produce the exometabolome in the phycosphere, which in turn reprograms gene expression in all partners. Recent advances in multi-omics toolboxes, single-cell isotopic analyses, and microfluidics have greatly enhanced our understanding of the functional and spatiotemporal orientation of algal microbiomes. Ecologically, algal–bacterial interactions manage the phytoplankton community structure, control HABs, and modulate carbon and nutrient fluxes in both marine and freshwater realms. Biotechnologically, engineered algal–bacterial consortia are a promising tool for enhancing biomass production, stabilizing large-scale cultivation, improving wastewater treatment, and upgrading biofuels and fine chemicals. Despite these notable research advances, the context- and species-dependent complexity of multispecies interactions remains a major obstacle to their practical modeling and scalable implementation. Integrative research frameworks that combine molecular, ecological, and bioengineering approaches are urgently needed to unlock the full potential of sustainable applications in the future. Full article

Aquaculture is the fastest-growing food production sector, supplying more than half of the world’s seafood and projected to expand further to meet rising global protein demands. Among the various pressures confronting this industry, harmful algal blooms (HABs) rank among the most alarming. Ichthyotoxic flagellates are microalgae known for producing toxins or inducing gill damage that leads to widespread fish mortality. Their increasing frequency poses a critical threat to aquaculture, emphasizing the urgent need for effective and environmentally sustainable strategies to regulate and mitigate these harmful episodes. This study investigated the responses of three ichthyotoxic flagellates renowned for impacting aquaculture operations (Prymnesium parvum, Heterosigma akashiwo, and Fibrocapsa japonica) and tested their susceptibility to two routinely applied chemical agents, hydrogen peroxide (H₂O₂) and copper sulfate (CuSO₄). Mortality, viability, and motility were assessed alongside trajectory-based metrics, including mean squared displacement (MSD) and probability density function (PDF). The results revealed species-specific sensitivities: P. parvum was highly susceptible to H₂O₂, while H. akashiwo and F. japonica were more susceptible to copper toxicity. Ichthyotoxic flagellates exhibited differential sensitivities to chemical treatments, with copper sulfate generally achieving lower EC₅₀ thresholds and greater inhibition of motility than hydrogen peroxide, except in P. parvum. The rapid attenuation of motility at sublethal concentrations highlights swimming behavior as a functional vulnerability, reinforcing the potential for behavior-based mitigation strategies that minimize chemical loading and reduce unintended impacts on cultured fish and surrounding ecosystems. Full article

Predicting algal proliferation in freshwater systems is crucial for effective water quality management and ecological sustainability. This study proposes a novel data-driven framework that integrates correlation-based feature ranking with a concatenation-enhanced artificial neural network (ANN) architecture to improve algae prediction accuracy. The analysis was conducted through a systematic evaluation of parameter relationships, employing Pearson’s correlation coefficient and standardized coefficients (Beta) to determine feature importance. Based on the magnitude of these coefficients, the input variables were progressively grouped into six feature sets, enabling a comparative assessment of predictive performance. The ANN models were trained and validated using root mean squared error (RMSE), mean absolute error (MAE) and Normalized Nash–Sutcliffe Efficiency (NNSE) as evaluation metrics. The results demonstrate that the fourth feature set, including chlorophyll-a, temperature, dissolved oxygen, total dissolved solids, and ammonia (NH₃), identified through combined Pearson and Beta analysis, achieved the lowest prediction errors and superior generalization performance. These findings highlight the effectiveness of feature selection guided by correlation and standardized coefficients in enhancing ANN performance for algae prediction. The proposed framework offers valuable insights for improving the predictive modeling of algal dynamics, thereby supporting proactive water quality monitoring and the sustainable management of aquatic ecosystems. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

Saxitoxin (STX), produced by certain harmful algal bloom (HAB) species, bioaccumulates through the food chain and can cause paralytic toxicity in humans, potentially resulting in fatal outcomes. To date, STX detection has primarily been conducted under laboratory-controlled conditions, and the availability of a gold-standard method for the proactive monitoring and prevention of HAB-induced secondary damage remains limited. Therefore, the present study introduces an electrochemical-based biosensor that is capable of early monitoring of STX in HAB-occurred environments. The conserved region of sxtA4, a nucleic acid precursor that is essential for STX biosynthesis, is immobilized on the sensing membrane surface in a sandwich structure. In this process, target detection is recognized as an electrochemical signal by a methylene blue-labeled detection probe, and the reliability of biosensing is supplemented by an electrochemical trend that is opposite to DNA binding. The application of an alternating current electrochemical flow technique achieves more sensitive detection at attomolar levels and rapid measurement within 10 min than a conventional DNA biosensor based on hybridization. In addition, the designed biosensing structure selectively detects STX-synthesizing and non-synthesizing dinoflagellates significantly. The proposed platform can utilize the identification of STX-induced secondary damage of HAB and provide insight into a field-ready biosensor based on its characterization and detection performance. Full article

The occurrence of marine heatwaves (MHWs) and phytoplankton blooms is accelerating under climate change, yet the frequency and drivers of their compound co-occurrence remain poorly understood. Using coastal-optimized satellite observations from 2003–2020, we mapped global compound MHW–phytoplankton bloom (MHW-PB) events across coastal large marine ecosystems and quantified their spatiotemporal trends and environmental predictors. Compound events are increasing at 4.8% yr⁻¹, driven primarily by a 6.5% yr⁻¹ rise in MHW frequency; a temporal shuffle test confirms this trend falls below random co-occurrence expectation, indicating biological suppression actively constrains compound event growth. The compound independence factor (CIF) reveals latitudinal heterogeneity: low-latitude upwelling systems show MHW–PB mutual exclusivity, while high-latitude and eutrophic coastal regions show positive co-occurrence tendency. Interpretable machine learning further shows that nutrient availability dominates bloom responses at low latitudes whereas light dominates at high latitudes, with MHW intensity exhibiting nutrient-dependent non-linear associations with bloom probability. Paradoxically, compound frequency accelerates nearly twice as fast in low latitudes (6.1% yr⁻¹) as in high latitudes (3.5% yr⁻¹), driven by rapid tropical MHW acceleration. These diverging regimes signal dual ecological risks: trophic mismatches in upwelling systems and escalating hypoxia and harmful algal bloom hazards in eutrophic coastal waters. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

Aquatic environments, whether freshwater, brackish, or marine, are increasingly disrupted, in terms of frequency, extent, geographic distribution, and duration, by the massive, worldwide proliferation of harmful and/or nuisance algae, the so-called Harmful algal blooms (HABs), which are a global phenomenon that poses a major threat to human and animal health and ecosystems [...] Full article

Marine mucilage events are intensifying in semi-enclosed seas under accelerating climate- and nutrient-driven pressures, yet their ecosystem-level consequences for aquaculture-linked coastal habitats remain insufficiently documented. This study provides an integrated spatiotemporal assessment of water quality, phytoplankton community structure, and biometric responses of Mytilus galloprovincialis during and after the 2025 mucilage outbreak in the Gulf of Erdek (Sea of Marmara, Türkiye). Mucilage accumulation was associated with sharp increases in turbidity, total suspended solids, and particulate organic matter, alongside declines in dissolved oxygen and pH. Phytoplankton assemblages exhibited marked seasonal restructuring: the mucilage period was characterized by the coexistence of mucilage-forming taxa, non-toxic bloomers, and multiple harmful algal bloom (HAB) groups, including DSP- and ASP-related species, whereas post-mucilage conditions were dominated by non-toxic diatoms with substantially reduced HAB representation. The dinoflagellate species representing the May period in terms of abundance were Noctiluca scintillans and Prorocentrum micans; the diatom species were Chaetoceros radiatus, Cylindrotheca closterium, Pseudo-nitzschia pseudodelicatissima, and Thalassiosira rotula; and the coccolithophore was Phaeocystis pouchetii. Mussel biometric analyses revealed biometric indices and condition values markedly below regional historical baselines during the mucilage event, alongside reduced meat yield, followed by pronounced compensatory growth during the post-mucilage period. Our findings demonstrate that mucilage acts as both a physical and biological stressor, driving short-term ecological shifts in phytoplankton diversity and imposing substantial but reversible physiological impacts on mussel stocks. These results underscore the need for continuous biodiversity monitoring frameworks that integrate mucilage dynamics, HAB occurrence, and aquaculture resilience in regions vulnerable to climate-enhanced organic aggregate formation. Full article

Harmful algal blooms (HABs) pose a serious threat to public health, aquaculture, and coastal ecosystems, making the development of tools for their rapid and specific detection a high priority. Laser-induced fluorescence (LIF) spectroscopy enables the assessment of characteristic photosynthetic pigments, offering a pathway to automated, high-throughput monitoring systems. Here, we investigate the temperature dependency of LIF spectra in the range of 20–80 °C to establish stable fluorescence fingerprints for the harmful microalgae Alexandrium catenella. Critically, we demonstrate that the relationship between temperature and both fluorescence intensity and spectral position remains consistent over 35 days of cultivation, independent of culture age. We performed complementary flow cytometric and pigment analyses (HPLC) to characterize the culture’s physiological state. Over the 35-day period, cell concentration increased 20-fold, while cell size, granularity, and fluorescence spectra remained stable. A transient decrease in fluorescence intensity observed on day 10 coincided with a drop in peridinin concentration, confirming the link between the spectral signal and pigment composition. Obtained results validate the use of this fluorescence fingerprint for the reliable identification of A. catenella without prior knowledge of the culture’s age—a key advantage for field applications. Furthermore, these fingerprints remained clearly distinguishable even when the culture was diluted with seawater to just 3% of its original volume, underscoring the potential sensitivity of this approach for early warning systems. Full article

Raphidiopsis raciborskii is one of the most widely reported cyanobacteria worldwide, responsible for dense blooms and cyanotoxin production. Classified as invasive, it has been documented across all continents except Antarctica. While its distribution has been extensively studied, abiotic factors have consistently emerged as the main determinants of its success, which are therefore the focus of the present study. The objective of the present review is to synthesize findings from both experimental and field-based studies to identify which are the key drivers of its dominance. In total, 30 abiotic factors were reported, reflecting the broad strategies of the species. Results show the temperature as a consistent universal factor (11–35 °C), while differences were found regarding nutrient dynamics. Particularly, nitrogen forms and N/P ratios predominated in field-based evidence, whereas photosynthetically active radiation was disproportionately emphasized within experimental studies under controlled conditions. Factors such as salinity and micronutrients, and synergistic interactions remain critically understudied, limiting predictive capacity under global change scenarios. Understanding which combinations of these drivers create favorable conditions is essential for anticipating bloom dynamics in order to establish management strategies for avoiding or mitigating the negative impact of them. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article